Optimization of Data-Dependent Acquisition Parameters in Mass Spectrometry for Oligonucleotide Identification

Area of Science:

• Biochemistry
• Molecular Biology
• Analytical Chemistry

Background:

• Accurate characterization of RNA modifications is vital for understanding RNA biology.
• Mass spectrometry, particularly data-dependent acquisition (DDA), is essential for identifying oligonucleotides (OGN) in epitranscriptomics.
• Optimization of DDA parameters is crucial for enhancing OGN identification.

Purpose of the Study:

• To optimize key DDA parameters on an Orbitrap Fusion Lumos mass spectrometer.
• To develop an iterative mass exclusion MS/MS acquisition method for improved OGN identification.
• To establish a robust workflow for confident OGN identification in MS-based epitranscriptomics.

Main Methods:

• Optimization of DDA parameters including full MS and MS/MS resolving power, top 15 MS/MS scans, and normalized HCD collision energy.
• Development of an iterative mass exclusion MS/MS acquisition method.
• Analysis of RNase T1 digested *E. coli* rRNA using optimized DDA settings.

Main Results:

• Optimal DDA performance achieved with 120,000 full MS resolution, 15,000 MS/MS resolution, top 15 MS/MS scans, and 30% normalized HCD collision energy.
• Identification of an average of 358 unique OGNs from *E. coli* rRNA.
• Achieved 58% rRNA sequence coverage, demonstrating enhanced identification capabilities.

Conclusions:

• Tailored DDA parameter optimization significantly improves OGN identification in mass spectrometry-based epitranscriptomics.
• The developed iterative mass exclusion method provides a robust workflow for confident OGN identification.
• This study advances the field of epitranscriptomics by providing a refined methodology for RNA characterization.